Sample screening and expression recognition method, neural network, equipment and storage medium

A screening method and neural network technology, applied in the computer field, can solve the problem that the accuracy of expression recognition cannot be effectively improved, and achieve the effect of improving the accuracy of recognition
CN110532880AActive Publication Date: 2019-12-03SHENZHEN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN UNIV
Publication Date
2019-12-03

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Abstract

The invention is applicable to the technical field of computers, and provides a sample screening and expression recognition method, a neural network, equipment and a storage medium. The method comprises the following steps: utilizing a multi-component sample; training neural networks, scooter for each iteration, determining a first sample distance between the anchor sample and the positive sampleand a second sample distance between the anchor sample and the negative sample, according to the distribution statistical characteristics of the sample distances; constructing a boundary condition forscreening the multi-tuple sample; the boundary condition being used for screening the multi-element group samples, and the reserved result obtained by screening entering the training of the next iterative step, so that the abnormal multi-element group samples can be screened out in the training process of the neural network, the influence of the abnormal multi-element group samples on the training result of the neural network is avoided, and the expression classification and recognition accuracy is improved.
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Description

technical field

[0001] The invention belongs to the technical field of computers, and in particular relates to a sample screening and expression recognition method, a neural network, equipment and a storage medium. Background technique

[0002] With the development of human-computer interaction, facial expression recognition has become a hot topic in recent decades. Today, deep learning neural networks use complex structures or multiple processing layers composed of multiple nonlinear transformations to abstract data at a high level and apply it to end-to-end image recognition and analysis. Expression recognition based on deep learning has surpassed traditional methods in various expression databases, and the effectiveness of training features has been improved through various network designs and algorithms such as data enhancement, metric learning, and network composition to improve its generalization recognition ability.

[0003] In the facial expression recognition algor...

Claims

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